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CMENRTPT vs CMENRF in SDTM Understanding CMENRTPT vs CMENRF in SDTM By Sarath Introduction When working with the Concomitant Medication (CM) domain in SDTM, it's crucial to understand how timing variables like CMENRTPT (End Relative to Reference Time Point) and CMENRF (End Relative to Reference Period) differ and when to use each. What is CMENRTPT? CMENRTPT indicates the relationship between the end of the medication and a specific time point , such as the start of treatment or a significant event (e.g., surgery). Controlled Terminology: BEFORE, AFTER, ONGOING, CONCURRENT Example: If a medication was stopped before surgery, CMENRTPT = "BEFORE" . What is CMENRF? CMENRF describes whether the medication ended ...

Resolving the SAS EG Transcoding Error Addressing the "Character Data Lost During Transcoding" Issue in SAS EG Author: Sarath Date: November 19, 2024 Introduction While working in SAS Enterprise Guide (SAS EG), you may encounter the error: "Some character data was lost during transcoding in the dataset." This issue typically arises when character data contains unsupported characters or is truncated due to insufficient column lengths. In this blog post, we'll explore the root causes and provide step-by-step solutions. Common Causes Unsupported Characters: The data contains special or non-ASCII characters not representable in the session encoding. Truncation: Character variables are too short to store the full data, leading to loss of information. ...

Advanced SAS Programming Techniques for SDTM Implementation Date: November 3, 2024 In the realm of clinical trials data management, SDTM (Study Data Tabulation Model) implementation requires sophisticated programming techniques to ensure data accuracy and compliance. This article explores advanced SAS programming methods that can streamline SDTM dataset creation and validation. 1. Efficient Variable Derivation Using Hash Objects Hash objects in SAS provide a powerful way to perform quick lookups and merges, especially useful when dealing with large SDTM datasets. data work.ae; if _n_ = 1 then do; declare hash h_dm(dataset: "sdtm.dm"); h_dm.definekey("usubjid"); h_dm.definedata("age", "sex", "race"); h_dm.definedone(); end; set raw.ae; rc = h_dm.find(); /* Continue processing */ run; Pro Tip:  Hash objects remain in memory throughout the DATA step, making them more efficient than tr...

Harnessing the Power of CALL EXECUTE in SAS for Dynamic Code Execution Harnessing the Power of CALL EXECUTE in SAS for Dynamic Code Execution As SAS programmers, we often encounter situations where we need to execute a certain procedure or set of steps multiple times, typically based on different subsets of data. Manually writing out code for each instance can be time-consuming, but SAS offers a powerful tool to make this process more efficient: CALL EXECUTE . What is CALL EXECUTE? CALL EXECUTE is a SAS routine that allows you to dynamically generate and execute SAS code during a data step’s execution. Instead of hardcoding the logic for every individual case, CALL EXECUTE can generate the code on the fly and execute it as part of the same data step. This technique is invaluable when you have repetitive tasks across different datasets, procedures, or even report generation. Basic Example: Dynamic PROC PRINT Execution Let...

Finding Duplicate Records Across SAS Datasets in an Entire Library Finding Duplicate Records Across SAS Datasets in an Entire Library Author: Sarath Date: October 10, 2024 Introduction In SAS programming, identifying and managing duplicate records within datasets is an essential part of data cleaning and quality control. However, when working with multiple datasets in an entire library, the task of finding duplicates becomes more complex. In this article, we will explore different ways to identify duplicate records in SAS datasets across an entire library using several approaches: PROC SORT , PROC SQL , and DATA STEP . Additionally, we will provide advanced techniques to streamline the process for multiple datasets in a library. Why Identify Duplicates? Duplicate records can cause significant issues in data analysis, leading to inaccurate results, inflated counts, or inco...

Comprehensive Guide to Define.xml Package Generation and QC Process Comprehensive Guide to Define.xml Package Generation and QC Process Author: Sarath Date: October 10, 2024 Introduction The Define.xml file, also known as the Case Report Tabulation Data Definition (CRT-DD), is a key component in regulatory submissions for clinical trials. It describes the metadata for the datasets submitted to regulatory agencies such as the FDA and EMA, providing transparency and traceability for clinical trial data. In this post, we’ll explore both the steps involved in generating the Define.xml package and the necessary Quality Control (QC) process to ensure its accuracy and compliance with regulatory requirements. What is Define.xml and Why Is It Important? The Define.xml file serves as the metadata backbone for clinical trial datasets submitted for regulatory review. It describes ...

The Power of RETAIN Statement in SAS Programming: Advantages and Use Cases The Power of RETAIN Statement in SAS Programming: Advantages and Use Cases Author: Sarath Date: October 10, 2024 Introduction The RETAIN statement in SAS is a powerful tool used to control the behavior of variables across iterations in a data step. Unlike standard SAS variables, which are reset at the beginning of each iteration, RETAIN allows you to preserve the value of a variable from one iteration to the next. In this blog post, we will explore the advantages and use cases of the RETAIN statement in SAS programming, including controlling variable order, and provide practical examples. Advantages of the RETAIN Statement Preserve Values Across Iterations: The primary advantage of using the RETAIN statement is its ability to retain values across data step iterations. This feature...